Optimal distributed generation location and sizing for loss minimization and voltage profile optimization using ant colony algorithm

Abstract

A system of power generation whereby the generating equipment is located close to the point of usage, thereby reducing losses and operation cost is called distributed generation (DG). However, it is imperative that DGs are sited such that the quality of power delivered is optimized and the total real power loss within the system minimized. This paper proposes an approach for optimum sizing and siting of DGs sizing in a power distribution system using Ant Colony Optimization (ACO) algorithm. To validate the algorithm the IEEE 30 bus standard test system was employed. A 92% decrease in real power loss within the system relative to the value before the connection of DGs was observed, while the minimum bus voltage increased from 0.656 per unit to 0.965 per unit. The results obtained from ACO are further verified by creating an ETAP model of the IEEE 30 bus system and simulating the impact of DG on the system. A significant reduction in total real power losses within the system and improvement in voltage profile was observed when the DGs are placed at the ACO derived sites relative to at other locations. Therefore, Ant Colony Algorithm can be used in deriving the optimum sites and sizes of DGs in a power distribution system.

Hybrid Heuristic Technique for Optimal Distributed Generation Integration in Distribution Systems

“…Vi is the actual voltage at bus i. The DG unit is represented as a negative PQ load that supplies real and reactive power to the distribution network [7]. The DG reactive power can be obtained by the following equation: 7 8/ ,09: ) , ..…”

“…In order to improve the voltage profile and reduce power losses in a part of a real distribution system, [5,6] uses the Genetic Algorithm (GA). The authors in [7] applied an Ant Colony Optimization (ACO) algorithm for optimal sizing and siting of DGs sizing in a power distribution network. A modified Artificial Bee Colony (ABC) algorithm is proposed in [8] to solve the DG optimization problem, Voltage and thermal constraints are considered and the algorithm was tested on 33-Bus distribution system.…”

Improved Genetic Algorithm for Phase-Balancing in Three-Phase Distribution Networks: A Master-Slave Optimization Approach

“…Owing to the importance of strategies for reducing the amount of energy loss in electrical distribution networks, multiple well-known approaches have been established. Some of these include (i) optimal sizing and installation of distributed generation [7]; (ii) optimal installation and sizing of reactive power compensators [5]; (iii) optimal grid reconfiguration [8]; and (iv) optimal phase-balancing strategies [2]. Each of the aforementioned methodologies can help distribution companies in significantly reducing the amount of power loss.…”

An Improved Crow Search Algorithm Applied to the Phase Swapping Problem in Asymmetric Distribution Systems

“…These power losses can exceed the capacity required to supply the demand, cause equipment to age, and increase investment and operating costs for network operators [7,9]. The importance of reducing power losses in distribution networks has established multiple approaches, such as (i) optimal placement and sizing of distributed generation [10], (ii) optimal capacitor placement and sizing [11], (iii) optimal network reconfiguration [12], (iv) optimal conductor sizing in distribution networks [13], (v) optimal power system restoration [14], and (vi) optimal phase swapping [15,16]. These strategies can significantly help distribution companies to reduce the number of power losses.…”